Abstract
Cell-identity switches, in which terminally differentiated cells are converted into different cell types when stressed, represent a widespread regenerative strategy in animals, yet they are poorly documented in mammals. In mice, some glucagon-producing pancreatic α-cells and somatostatin-producing δ-cells become insulin-expressing cells after the ablation of insulin-secreting β-cells, thus promoting diabetes recovery. Whether human islets also display this plasticity, especially in diabetic conditions, remains unknown. Here we show that islet non-β-cells, namely α-cells and pancreatic polypeptide (PPY)-producing γ-cells, obtained from deceased non-diabetic or diabetic human donors, can be lineage-traced and reprogrammed by the transcription factors PDX1 and MAFA to produce and secrete insulin in response to glucose. When transplanted into diabetic mice, converted human α-cells reverse diabetes and continue to produce insulin even after six months. Notably, insulin-producing α-cells maintain expression of α-cell markers, as seen by deep transcriptomic and proteomic characterization. These observations provide conceptual evidence and a molecular framework for a mechanistic understanding of in situ cell plasticity as a treatment for diabetes and other degenerative diseases.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
RNA-seq data that support the findings of this study have been deposited in NCBIs Gene Expression Omnibus (GEO) under accession codes GSE117454 (bulk RNA-seq) and GSE123844 (scRNA-seq). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD011933.
References
Mauda-Havakuk, M. et al. Ectopic PDX-1 expression directly reprograms human keratinocytes along pancreatic insulin-producing cells fate. PLoS One 6, e26298 (2011).
Zhu, S. et al. Human pancreatic beta-like cells converted from fibroblasts. Nat. Commun. 7, 10080 (2016).
Lima, M. J. et al. Suppression of epithelial-to-mesenchymal transitioning enhances ex vivo reprogramming of human exocrine pancreatic tissue toward functional insulin-producing β-like cells. Diabetes 62, 2821–2833 (2013).
Lee, J. et al. Expansion and conversion of human pancreatic ductal cells into insulin-secreting endocrine cells. eLife 2, e00940 (2013).
Chera, S. & Herrera, P. L. Regeneration of pancreatic insulin-producing cells by in situ adaptive cell conversion. Curr. Opin. Genet. Dev. 40, 1–10 (2016).
Thorel, F. et al. Conversion of adult pancreatic α-cells to β-cells after extreme β-cell loss. Nature 464, 1149–1154 (2010).
Chera, S. et al. Diabetes recovery by age-dependent conversion of pancreatic δ-cells into insulin producers. Nature 514, 503–507 (2014).
Bramswig, N. C. et al. Epigenomic plasticity enables human pancreatic α to β cell reprogramming. J. Clin. Invest. 123, 1275–1284 (2013).
Spijker, H. S. et al. Conversion of mature human β-cells into glucagon-producing α-cells. Diabetes 62, 2471–2480 (2013).
Yoneda, S. et al. Predominance of β-cell neogenesis rather than replication in humans with an impaired glucose tolerance and newly diagnosed diabetes. J. Clin. Endocrinol. Metab. 98, 2053–2061 (2013).
White, M. G. et al. Expression of mesenchymal and α-cell phenotypic markers in islet β-cells in recently diagnosed diabetes. Diabetes Care 36, 3818–3820 (2013).
Butler, A. E. et al. Marked expansion of exocrine and endocrine pancreas with incretin therapy in humans with increased exocrine pancreas dysplasia and the potential for glucagon-producing neuroendocrine tumors. Diabetes 62, 2595–2604 (2013).
Xiao, X. et al. Endogenous reprogramming of alpha cells into beta cells, induced by viral gene therapy, reverses autoimmune diabetes. Cell Stem Cell 22, 78–90 (2018).
Ben-Othman, N. et al. Long-term GABA administration induces alpha cell-mediated beta-like cell neogenesis. Cell 168, 73–85 (2017).
Li, J. et al. Artemisinins Target GABAA receptor signaling and impair α cell identity. Cell 168, 86–100 (2017).
Zhou, Q. & Melton, D. A. Pancreas regeneration. Nature 557, 351–358 (2018).
van der Meulen, T. et al. Artemether does not turn α cells into β cells. Cell Metab 27, 218–225 (2018).
Zhu, Y., Liu, Q., Zhou, Z. & Ikeda, Y. PDX1, Neurogenin-3, and MAFA: critical transcription regulators for beta cell development and regeneration. Stem Cell Res. Ther. 8, 240 (2017).
Matsuoka, T. A. et al. Mafa enables Pdx1 to effectively convert pancreatic islet progenitors and committed islet α-cells into β-cells in vivo. Diabetes 66, 1293–1300 (2017).
Dorrell, C. et al. Human islets contain four distinct subtypes of β cells. Nat. Commun. 7, 11756 (2016).
Bosco, D., Orci, L. & Meda, P. Homologous but not heterologous contact increases the insulin secretion of individual pancreatic B-cells. Exp. Cell Res. 184, 72–80 (1989).
Takebe, T. et al. Vascularized and functional human liver from an iPSC-derived organ bud transplant. Nature 499, 481–484 (2013).
Hematti, P., Kim, J., Stein, A. P. & Kaufman, D. Potential role of mesenchymal stromal cells in pancreatic islet transplantation. Transplant. Rev. (Orlando) 27, 21–29 (2013).
Hilderink, J. et al. Controlled aggregation of primary human pancreatic islet cells leads to glucose-responsive pseudoislets comparable to native islets. J. Cell. Mol. Med. 19, 1836–1846 (2015).
Segerstolpe, Å. et al. Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metab. 24, 593–607 (2016).
Damond, N. et al. Blockade of glucagon signaling prevents or reverses diabetes onset only if residual β-cells persist. eLife 5, e13828 (2016).
Neumann, U. H., Denroche, H. C., Mojibian, M., Covey, S. D. & Kieffer, T. J. Insulin knockout mice have extended survival but volatile blood glucose levels on leptin therapy. Endocrinology 157, 1007–1012 (2016).
Syed, S. K. et al. Ectonucleotidase NTPDase3 is abundant in pancreatic β-cells and regulates glucose-induced insulin secretion. Am. J. Physiol. Endocrinol. Metab. 305, E1319–E1326 (2013).
Andersson, S. A. et al. Reduced insulin secretion correlates with decreased expression of exocytotic genes in pancreatic islets from patients with type 2 diabetes. Mol. Cell. Endocrinol. 364, 36–45 (2012).
Chu, K. Y., Li, H., Wada, K. & Johnson, J. D. Ubiquitin C-terminal hydrolase L1 is required for pancreatic beta cell survival and function in lipotoxic conditions. Diabetologia 55, 128–140 (2012).
Inagaki, N., Kuromi, H. & Seino, S. PACAP/VIP receptors in pancreatic β-cells: their roles in insulin secretion. Ann. N. Y. Acad. Sci. 805, 44–51; discussion 52–43 (1996).
Muller, Y. L. et al. A cis-eQTL in PFKFB2 is associated with diabetic nephropathy, adiposity and insulin secretion in American Indians. Hum. Mol. Genet. 24, 2985–2996 (2015).
Kane, M. A. et al. CrbpI modulates glucose homeostasis and pancreas 9-cis-retinoic acid concentrations. Mol. Cell. Biol. 31, 3277–3285 (2011).
Arda, H. E. et al. Age-dependent pancreatic gene regulation reveals mechanisms governing human β cell function. Cell Metab. 23, 909–920 (2016).
Cigliola, V. et al. Pancreatic islet-autonomous insulin and smoothened-mediated signalling modulate identity changes of glucagon+ α-cells. Nat. Cell Biol. 20, 1267–1277 (2018).
Qiu, X. et al. Single-cell mRNA quantification and differential analysis with Census. Nat. Methods 14, 309–315 (2017).
Kim-Muller, J. Y. et al. Aldehyde dehydrogenase 1a3 defines a subset of failing pancreatic β cells in diabetic mice. Nat. Commun. 7, 12631 (2016).
Kracht, M. J. et al. Autoimmunity against a defective ribosomal insulin gene product in type 1 diabetes. Nat. Med. 23, 501–507 (2017).
Skowera, A. et al. CTLs are targeted to kill β cells in patients with type 1 diabetes through recognition of a glucose-regulated preproinsulin epitope. J. Clin. Invest. 118, 3390–3402 (2008).
Kulkarni, R. N. & Stewart, A. F. Summary of the Keystone islet workshop (April 2014): the increasing demand for human islet availability in diabetes research. Diabetes 63, 3979–3981 (2014).
van der Torren, C. R. et al. Immunogenicity of human embryonic stem cell-derived beta cells. Diabetologia 60, 126–133 (2017).
van der Torren, C. et al. Innate and adaptive immunity to human beta cell lines: implications for beta cell therapy. Diabetologia 59, 170–175 (2016).
Schaffer, A. E. et al. Nkx6.1 controls a gene regulatory network required for establishing and maintaining pancreatic beta cell identity. PLoS Genet. 9, e1003274 (2013).
Herrera, P. L. Adult insulin- and glucagon-producing cells differentiate from two independent cell lineages. Development 127, 2317–2322 (2000).
Rezania, A. et al. Reversal of diabetes with insulin-producing cells derived in vitro from human pluripotent stem cells. Nat. Biotechnol. 32, 1121–1133 (2014).
Thorel, F. et al. Normal glucagon signaling and β-cell function after near-total α-cell ablation in adult mice. Diabetes 60, 2872–2882 (2011).
Zhou, Q., Brown, J., Kanarek, A., Rajagopal, J. & Melton, D. A. In vivo reprogramming of adult pancreatic exocrine cells to β-cells. Nature 455, 627–632 (2008).
Mathe, Z. et al. Tetracycline-regulated expression of VEGF-A in beta cells induces angiogenesis: improvement of engraftment following transplantation. Cell Transplant. 15, 621–636 (2006).
Sun, J., Nishiyama, T., Shimizu, K. & Kadota, K. TCC: an R package for comparing tag count data with robust normalization strategies. BMC Bioinformatics 14, 219 (2013).
Wiśniewski, J. R., Zougman, A., Nagaraj, N. & Mann, M. Universal sample preparation method for proteome analysis. Nat. Methods 6, 359–362 (2009).
Paulo, J. A., O’Connell, J. D. & Gygi, S. P. A triple knockout (TKO) proteomics standard for diagnosing ion interference in isobaric labeling experiments. J. Am. Soc. Mass Spectrom. 27, 1620–1625 (2016).
McAlister, G. C. et al. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal. Chem. 86, 7150–7158 (2014).
Paulo, J. A. & Gygi, S. P. A comprehensive proteomic and phosphoproteomic analysis of yeast deletion mutants of 14-3-3 orthologs and associated effects of rapamycin. Proteomics 15, 474–486 (2015).
Huttlin, E. L. et al. A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 1174–1189 (2010).
Beausoleil, S. A., Villén, J., Gerber, S. A., Rush, J. & Gygi, S. P. A probability-based approach for high-throughput protein phosphorylation analysis and site localization. Nat. Biotechnol. 24, 1285–1292 (2006).
Elias, J. E. & Gygi, S. P. Target-decoy search strategy for mass spectrometry-based proteomics. Methods Mol. Biol. 604, 55–71 (2010).
Elias, J. E. & Gygi, S. P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207–214 (2007).
McAlister, G. C. et al. Increasing the multiplexing capacity of TMTs using reporter ion isotopologues with isobaric masses. Anal. Chem. 84, 7469–7478 (2012).
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).
Masini, M. et al. Ultrastructural morphometric analysis of insulin secretory granules in human type 2 diabetes. Acta Diabetol. 49 (suppl. 1), S247–S252 (2012).
Acknowledgements
We are grateful to R. Stein for reading the manuscript, and constructive comments and suggestions. We thank C. Gysler for technical help, J.-P. Aubry-Lachainaye for FACS assistance, C. Delucinge-Vivier and M. Docquier for RNA-seq. We thank Q. Zhou for viral vectors, R. Millican and P. Cain for anti-GCGR antibody, and R. Nano and L. Piemonti for human donor samples. Human islets were provided through the JDRF award 31-2008-416 (ECIT Islet for Basic Research program) or the NIDDK-funded Integrated Islet Distribution Program (IIDP) at City of Hope, National Institutes of Health (NIH) Grant no. DK098085. This work was funded with grants from the Research Council of Norway (NFR 247577) and the Novo Nordisk Foundation (NNF15OC0015054) to S.C.; NIH/NIDDK grant DK098285 to J.A.P.; Bergen Forskningsstiftelse (BFS2014REK02) and the Western Norway Regional Health Authority (Bergen Stem Cell Consortium) and the Novo Nordisk Foundation (NNF17OC0027258) to H.R.; NIH/NIDDK (Human Islet Research Network, DK104209 and DK108132), the Juvenile Diabetes Research Foundation (SRA-2015-67-Q-R), the Fondation Privée des HUG – Confirm Award, the Fondation Aclon, and the Swiss National Science Foundation (NRP63 no. 406340-128056, no. 310030_152965 and the Bonus of Excellence grant no. 310030B_173319) to P.L.H.
Author information
Authors and Affiliations
Contributions
K.F. conceived and performed the experiments and analyses, and wrote the manuscript; S.C. analysed the omics data and wrote the manuscript; L.v.G. and L.G. analysed the omics data, and edited the manuscript; N.D. conceived and performed some experiments and analyses, and edited the manuscript; D.O. analysed data and wrote the manuscript; A.M.J., K.F. and B.O.R. performed and analysed immunogenicity tests; H.V. and H.R. prepared samples and contributed to the mass spectrometry study; J.A.P. performed the TMT-labelling experiment and mass spectrometry analysis; T.B. and D.B. generated human islet preparations and contributed to discussions; C.D. and M.G. generated antibodies, established cell-sorting strategies and contributed to discussions; F.T. and P.L.H. conceived the experiments, interpreted the observations and wrote the manuscript.
Corresponding author
Ethics declarations
Competing interests
Oregon Health & Science University has commercially licensed one of the antibodies described here (HIC1-2B4/HPi2); C.D. and M.G. are inventors of this antibody. This potential conflict of interest has been reviewed and managed by OHSU.
Additional information
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Sorted islet cell-types are highly pure and efficiently labelled.
a, Human islets were dissociated into single cells and antibody-labelled for FACS sorting. Representative FACS plots illustrating cells labelled with pan-endocrine marker HIC1-2B4 and non-β endocrine marker HIC3-2D128,20. Sorted islet cell fractions were immunostained for insulin (INS), glucagon (GCG), somatostatin (SST), pancreatic polypeptide (PPY) and ghrelin (GHRL), and counted. All sorted cells were monohormonal. Other staining data at higher magnification or tile scanning are shown in Supplementary Figs. 1–3. Scale bars, 250 μm. All FACS results (n = 42 different donors) are also summarized in Supplementary Table 2. b, Sorting results showing cell purity of islet cell types. Purity of α-cells and β-cells in most islet preparations displayed a 99% purity (98.9 ± 0.8% and 98.9 ± 0.5%, respectively), but PPY+ γ-cells showed great batch-to-batch variability (up to 98% purity), with α-cell or ghrelin+ ε-cell contamination, but without β-cells (less than 0.5%). Only sorted cells with high purity (>96% for α/β cells and >90% for γ-cells) were used in experiments (Supplementary Table 2). c, qPCR of hormonal expression (INS, GCG, SST and PPY) in all sorted fractions (α-, β- and γ-cells) that were used for experiments. qPCR of INS in sorted α-cells shows very rare contamination of β-cells, which is consistent with estimated purity calculated by a previous method8. d, Sorted α-cells were transduced with Ad-GFP, reaggregated into pseudoislets and cultured for 7 or 14 days. To evaluate transduction efficiency, pseudoislets were dissociated again into single cells and FACS analysed. More than 99% of α-cells expressed GFP, whereas non-transduced α-cells did not. FACS plots are representative of three independent donor samples. All values of percentage purity or contamination (a–c) are mean ± s.d. n = 41 donors for α-cells, n = 42 donors for β-cells and n = 5 donors for γ-cells. For details, see Supplementary Table 2.
Extended Data Fig. 2 Reaggregation of dispersed purified human β-cells.
a, Pure β-cells were labelled with GFP and traced in three different culture conditions: in monolayer (‘single β’), β-cell-only aggregation (‘β’), or β-cell aggregation with stromal cells including HUVECs and MSCs (‘β + HM’). Live imaging at indicated days (middle panels) and immunofluorescence at day 7 (right panels) show β-cell-only pseudoislets were self-organized by day 5, whereas β + HM aggregates were constituted in only 1 day. β-cells in β + HM pseudoislets located at the periphery, whereas HM cells formed the core of the aggregates (red and blue, respectively). b, To determine the optimal number of β-cells per pseudoislet, GFP+ transduced β-cells were seeded on aggregation-plate-wells at the indicated densities. After 7 days of culture, aggregates were analysed and found to be uniform in size. Pseudoislet size correlated with the number of cells seeded per well. Human islet cell aggregates with a diameter of 100–150 μm, consisting of 1,000 cells, have been shown to have a comparable function to native islets24; we thus performed reaggregation experiments at 1,000 β-cells per pseudoislet (1,000 β-cells, 129.6 ± 3.1 μm). β-cell aggregates with HM were also analysed. n = 8 pseudoislets for 500 β-cells, n = 8 pseudoislets for 1,000 β-cells, n = 9 pseudoislets for 2000 β-cells, n = 8 pseudoislets for 3,000 β-cells, and n = 52 pseudoislets for 1,000 β-cells + 400 HUVECs + 100 MSCs. c, Immunofluorescence at indicated time points in β-cell pseudoislets and β-cell + HM pseudoislets. d, TUNEL staining (green) showed rare apoptotic cells (0.8%) in β-cell aggregates after 7 days of culture. e, qPCR analyses of INS and PDX1 expression in monolayer and aggregated β-cells showing higher expression of β-cell markers in reaggregated β-cells. Data are expressed as fold change relative to the value in single β-cells. *P = 0.022 in qPCR for INS, *P = 0.026 in qPCR for PDX1, Mann–Whitney test, two-tailed. n = 6 donor samples. f, ELISA measurements of static glucose-stimulated human insulin release at 3 mM (low) and 20 mM (high) glucose showing glucose-responsive C-peptide secretion in both β and β + HM aggregates, but not in single β-cells. *P = 0.012, **P = 0.0037, ****P < 0.0001, two-way repeated-measures ANOVA with Holm–Sidak’s multiple comparisons test, n = 5 for single β-cells, n = 8 for β and β + HM, n = 6 for native islets (all are biological replications from different donors). g, Stimulation index in glucose-stimulated insulin secretion in f exhibiting comparable values among pseudoislets of β and β + HM and native islets. *P = 0.045, **P = 0.0089, *one-way ANOVA with Benjamini, Krieger and Yekutieli’s multiple comparisons test. Images are representative of five (a, c, d) or three (b) independent experiments. ns, not significant. Data are mean ± s.e.m. Scale bars, 25 μm (a), 50 μm (c, d) or 100 μm (b).
Extended Data Fig. 3 Assessment of the effect of transcription factor expression on insulin production in human α-cells.
a, Representative immunofluorescence images at 7 days of reaggregation. To determine the best α-to-β-cell reprogramming factors, human α-cells were transduced with adenoviral vectors, including PDX1, MAFA and NKX6-1, in all combinations, and then reaggregated. Images are representative of n = 38 donors for αGFP and αPM, n = 5 for αPDX1 and α3TFs, n = 3 for αMAFA, αNKX6-1, αMN6 and αPN6. b, qPCR analysis of human INS expression in α-cells transduced with indicated reprogramming factors, 7 days after aggregation. ****P < 0.0001, ***P = 0.0006 versus αGFP control, one-way ANOVA with Tukey’s multiple comparisons test. n = 7 for αGFP, αPDX1, αPM and α3TFs, n = 5 for αMAFA, αNKX6-1, αMN6, αPN6: all are biological replications from different donors. c, Percentages of bihormonal cells (expressing insulin and glucagon) in αPM single cells, αPM-only pseudoislets and αPM+HM pseudoislets. One-way ANOVA with Tukey’s multiple comparisons, n = 3 from different donors. d, qPCR analyses in αGFP and αPM pseudoislets cultured for 7 days. αPM cells have less glucagon expression than αGFP pseudoislets, but ARX expression is still maintained. *P = 0.015, Mann–Whitney test, two-tailed, n = 6 from different donors per group. e, qPCR analysis for INS expression in αGFP and αPM cells cultured in monolayer or pseudoislets. αPM single cells express less insulin than αPM pseudoislets; αGFP controls display only background levels. ***P = 0.0002, †††P = 0.0009, one-way ANOVA with Tukey’s multiple comparisons test, n = 4 from different donors. f, Live-cell imaging of in vitro pseudoislet formation using αPM and HM cells. Aggregation is faster with HM cells. g, Single α-cells transduced with PDX1 and MAFA show very rare reprogramming events (that is, insulin production), whereas re-aggregated α-cells display high reprogramming efficiency. α-cells (GFP+, green) locate at the periphery of pseudoislets containing also HM cells (HUVECs/MSCs: only DAPI+, white). h, Immunofluorescence for PDX1, NKX6-1 and insulin on αGFP, αPM and αPM + HM pseudoislets after 7 days of culture. Reprogrammed α-cells express insulin (red) and PDX1 (green), but not NKX6-1 (blue) in αPM and αPM + HM aggregates. i, TUNEL staining (green) reveals almost no apoptosis in α-cell pseudoislets in 7-day cultures (1.8% in αGFP, 1.4% in αPM, 1.6% in αPM + HM). j, Staining for proliferation marker pHH3 (green) reveals almost no proliferation (<1%) in both αGFP and αPM pseudoislets in 7-day cultures. Images are representative of three different donors (f–i). Data are mean ± s.e.m. Scale bars, 25 μm.
Extended Data Fig. 4 γ-cell reprogramming and in vitro kinetics of cell number and gene expression levels in pseudoislets.
a, Live-cell imaging during reaggregation into pseudoislets of GFP-transduced γ-cells. As in α-cell pseudoislets (Fig. 1), sorted γ-cells were transduced with adenoviral vectors, and then seeded on reaggregation plates. b, Reprogramming efficiency into insulin expression (percentage of insulin+ out of GFP+ cells). Seven days after aggregation, γ-cells transduced with the indicated reprogramming factors show the highest reprogramming incidence (PM combination). *P = 0.046, ****P < 0.0001 versus γGFP control; †††P = 0.0008 versus γPM, one-way ANOVA with Tukey’s multiple comparisons test. n = 3 from different donors. c, qPCR analysis of human INS gene expression in α-cell pseudoislets. Seven days after aggregation, γ-cells transduced with the indicated reprogramming factors show the highest reprogramming incidence (PM combination). ***P = 0.0009 versus γGFP control, one-way ANOVA with Tukey’s multiple comparisons test. n = 3 from different donors. Data are mean ± s.e.m. d, Immunofluorescence of γGFP and γPM pseudoislets after 7 days in culture. Most insulin-expressing reprogrammed γ-cells maintain PPY expression (blue in inset). e, Live-cell imaging of aggregated transduced γ-cells. γPM + HM pseudoislets form faster than γ-cell-only pseudoislets (a). Scale bar, 25 μm. f, g, Control γ-cells in γGFP + HM pseudoislets do not reprogram (insulin production) (<1%; left panel in f), yet PM-transduced γ-cells become insulin-producers in γPM + HM pseudoislets (right panel in f). The architecture of γPM + HM pseudoislets is similar to that of αPM + HM pseudoislets; however, γ-cell reprogramming efficiency in γPM + HM clusters is lower than in γPM-only pseudoislets (g). *P = 0.029, Mann–Whitney test, two-tailed. n = 4 from different donors. Data are mean ± s.e.m. Scale bars, 25 μm. h, Glucose-stimulated insulin secretion: γ-cells in γPM-only pseudoislets efficiently secrete insulin in response to glucose stimulation in vitro, but not γGFP pseudoislets. Notably, they secrete more insulin than α-cells in αPM + HM pseudoislets (1.24 fmol per 103 cells versus 0.27 fmol per 103 cells for converted α-cells; h and Fig. 1f). ns, P = 0.82, **P = 0.0043, two-way repeated-measures ANOVA with Holm–Sidak’s multiple comparisons test. n = 3 donor samples. Data are mean ± s.e.m. Scale bars, 25 μm. All images are representative of three (a, e) or four (d, f) independent experiments. i, DNA content was measured by Pico-green tests to assess cell number kinetics in pseudoislets in vitro. Cell numbers dropped mainly between 1 and 2 weeks in α- and β-cell pseudoislets. j, Expression of human INS and adenoviral vector mouse Pdx1 and Mafa were evaluated by qPCR at indicated time points. Notably, insulin expression increased with time. Exogenous adenoviral Pdx1 and Mafa expression levels were also maintained for 8 weeks in vitro. n = 3 from different donors (i, j). Data are mean ± s.d. (i, j).
Extended Data Fig. 5 αPM + HM pseudoislets secrete human insulin after glucose stimulation in healthy NSG host mice.
a, αPM + HM pseudoislets generated from non-diabetic donor samples were transplanted under the kidney capsule of non-diabetic NSG mice. b, Four weeks after transplantation, in vivo glucose-stimulation tests were performed. Circulating human C-peptide levels are higher after glucose injection compared to before the injection. *P = 0.031, Mann–Whitney test, two-tailed. n = 10 from 6 different donors. c, Immunostaining picture of control αGFP + HM grafts 4 weeks after transplantation under the kidney capsule of host NSG mice; there is almost no reprogramming into insulin production (<1%). d–g, αPM + HM grafts show vascular formation around the grafts and retain abundant GFP-labelled insulin-expressing cells 2 weeks (d) and 4 weeks (e) after transplantation. Notably, αPM + HM grafts display increased reprogramming efficiency (% of insulin+ cells out of GFP+ cells; e, f) and fractions of monohormonal INS+ cell (4 weeks after transplantation; e, g). *P = 0.029, two-tailed Mann–Whitney test. n = 4 from different donors. Data are mean ± s.e.m. Scale bars, 25 μm. Images are representative of six (a), three (c), two (d) or four (e) different donors.
Extended Data Fig. 6 A small number of human α-cell pseudoislets is sufficient to ameliorate diabetes in mice.
a, Experimental timeline of experiment 2. NSG mice were made diabetic with STZ, and a week later were transplanted with 200–1,000 αPM + HM pseudoislets (STZ αPM+HM). As controls, STZ-diabetic mice were implanted with either no graft (STZ no graft) or the same number of native human islets (STZ islets). Non-diabetic NSG control mice were also monitored. Nephrectomy (Nx) was performed 4 weeks after transplantation for graft removal. See Supplementary Table 5. b, Random-fed blood glucose levels. c, Areas under the curves (AUC) during the engraftment period (indicated in yellow in b) reveal a significant hyperglycaemia lowering in mice receiving native islets. *P = 0.022, one-way repeated-measures ANOVA with Holm–Sidak’s multiple comparisons test. d, Body weight changes after STZ injection. e, AUC of body weight changes during engraftment (indicated in yellow in d) reveal a significant body weight gain in mice receiving intact islets (STZ islet group) and αPM + HM pseudoislets (STZ αPM group). *P = 0.042, **P = 0.003, ****P < 0.0001, one-way repeated-measures ANOVA with Holm–Sidak’s multiple comparisons test. n = 3 mice, engrafted with materials from three different donors in all groups (b–e). f–i, Glucose tolerance test at 4 weeks after transplantation (f) and 2 weeks after graft removal (g). Engrafted mice display recovery after 3 h (f), but this capacity is lost after graft removal (g). Analyses of AUCs in f and g are shown in h. There is partial STZ-diabetes recovery in STZ αPM mice, but not in the STZ no-graft group (h, left). After graft removal, both STZ islets and STZ αPM groups become hyperglycaemic again (h, right), proving that improvement in glucose tolerance is graft-dependent. i, Blood human C-peptide levels in mice measured before (0 min) and after (15 min) glucose injection (during glucose tolerance test in f). Glucose-responsive C-peptide release is observed in mice bearing human islets or αPM pseudoislets. In f, ***P = 0.0007, ###P = 0.0009, ****P < 0.000, versus STZ no graft, two-way repeated-measures ANOVA with Dunnett’s multiple comparisons test. In h, **P = 0.0025, ****P = 0.00007, ##P = 0.0014, ####P = 0.00002, one-way ANOVA with Benjamini, Krieger and Yekutieli’s multiple comparisons test. In i, **P = 0.0015, ****P = 5 × 10−8, two-way repeated-measures ANOVA with Holm–Sidak’s multiple comparisons test. In f–i, n = 3 mice grafted from different donors (STZ αPM, STZ islets), n = 7 mice (STZ no graft), n = 6 mice (no STZ no graft). j, Immunofluorescence of pancreas in the NSG RIP-DTR mouse that was transplanted with αPM pseudoislets (exp. 3: DT + αPM, Fig. 3b) shows that endogenous mouse β-cells were well-ablated and did not regenerate, suggesting improvement of diabetic symptoms was dependent on the human αPM graft. k, Immunofluorescence of pseudoislet kidney grafts in STZ αPM (top) and STZ islets (bottom) mice, 4 weeks after transplantation. Monohormonal insulin-producing cells with GFP-tracer are abundant in the engrafted αPM + HM pseudoislets. l, Body weight changes in experimental animals of Fig. 3b (exp. 3). After diphtheria toxin injection, untransplanted diabetic controls (DT + no graft) exhibited continuous weight loss, whereas there is body weight gain after transplantation with intact islets or αPM pseudoislets. m, n, ipGTT at 7 weeks after transplantation (related to Fig. 3b–d) shows significantly improved glucose tolerance in both DT + αPM and DT + islets groups. *P = 0.0338, **P = 0.002, ****P < 0.0001, versus DT + no graft, two-way repeated-measures ANOVA with Dunnett’s multiple comparisons test. Groups are indicated by coloured lines, as in Fig. 3b, c. n = 3 for DT + islets, n = 1 for DT + αPM, n = 5 for DT + no graft, and n = 5 for no DT + no graft (l, m). n = 2 for DT + islets, n = 1 for DT + αPM, n = 3 for DT + no graft, and n = 4 for no DT + no graft (n). o, αPM pseudoislets grafted into mouse kidney show innervation (TH+) and vascularization (CD31+) 1 month after transplantation. p, pHH3 staining on grafts of αPM + HM 4 weeks after transplantation shows almost no proliferative cells in grafts (<1%). q, r, Immunofluorescence of grafted αPM pseudoislets shows reprogrammed α-cells express insulin as well as GFP-tracer at 3 months (q) and 6 months (r) after transplantation, confirming a stable phenotype of αPM cells under an in vivo environment. Left panels in r are confocal tile-scan images that were merged as a maximum projection. We did not detect SST-, PPY- or GHRL-positive cells. Data are mean ± s.e.m. (except in l, in which error bars denote s.d.). Scale bars, 50 μm. Images are representative of nine different diabetic mice after DT injection (j), from n = 4 (k), n = 3 (o, p), n = 1 (q), or n = 1 (r) donors.
Extended Data Fig. 7 Reprogrammed α-cells from diabetic donors lead to diabetes remission in mice.
a, Human islets from T2D donors were dissociated into single cells analysed by flow cytometry. Representative FACS plots showing cells labelled with the pan-endocrine marker HIC1-2B4 and non-β-cell endocrine marker HIC3-2D1220. The purity of sorted islet cells was evaluated. Data are mean ± s.d. FACS plots are representative of three T2D donors. b, c, Reprogramming efficiency into insulin production (percentage of insulin+ GFP+ out of GFP+ cells in b) and qPCR analysis of human INS expression (c), 7 days after aggregation of α-cells transduced with the indicated reprogramming factors. PDX1 and MAFA combined (αPM) trigger the highest reprogramming efficiency. *P = 0.031, ****P < 0.0001 versus αGFP control; ##P = 0.0055 versus αPM, one-way ANOVA with Tukey’s multiple comparisons test. n = 3 different T2D donors. d, Representative immunostaining at culture day 7 in αGFP and αPM pseudoislets from three T2D donors. e, Experimental timeline of experiment 5. Sequential transplantation was performed using human α-cells of T2D donors to rescue STZ-diabetes, followed by anti-glucagon receptor (GCGR) antibody treatment for 2 weeks. Graft was removed 1 week after anti-GCGR antibody therapy. The next week, GCGR antibody treatment was stopped. f, Summary of transplantation experiment using consecutive islet preparations from two different T2D donors. First, 2,300 αPM + HM pseudoislets were transplanted under the kidney capsule of a diabetic STZ-treated NSG mouse. Two weeks later, 1,450 additional αPM + HM pseudoislets were generated and engrafted into the same kidney. g, Random-fed blood glucose levels. Before glucagon inhibition, there is a mild amelioration of hyperglyacemia in the mouse bearing two grafts of T2D αPM pseudoislets, but this is less marked than in the mouse that received T2D islets. After GCGR antibody treatment, glycaemia markedly and quickly drops in both engrafted mice. Graft removal quickly leads to hyperglycaemia, even under glucagon signalling inhibition. h, Glucose tolerance tests before the second transplantation (3 weeks after first transplantation), 4 weeks after the second transplantation, and after graft removal (post Nx). There is improved glucose tolerance in diabetic mice transplanted with αPM + HM pseudoislets (red line) compared with untransplanted diabetic controls (black line). i, Circulating human C-peptide after the first transplantation. The data after the second transplantation are also shown in Fig. 2e. j, In vivo stimulation index (of insulin secretion) after a glucose challenge is similar in native T2D islets and T2D αPM pseudoislets. k, Immunofluorescence of engrafted T2D αPM pseudoislets. Insulin-expressing (red) reprogrammed α-cells (GFP+, green) are abundant and do not contain glucagon (blue). l, m, Reprogramming efficiency (l) and percentage of monohormonal insulin-producing cells (m) in αPM pseudoislets from T2D donors before (pre Tx) and after (post Tx) transplantation. **P = 0.0082, ***P = 0.0005, two-tailed paired t-test. n = 3 donors with T2D from first and second grafts and independent cohort. n, Immunofluorescence for PDX1, MAFA and INS in the graft of T2D intact islets (left) or T2D αPM + HM cells (right) 9 weeks after transplantation. Reprogrammed α-cells express insulin (red), PDX1 (green) and MAFA (blue). o, qPCR analyses in αGFP, αPM aggregates in vitro (before transplantation) and αPM+HM pseudoislets in vivo (after transplantation). Transplanted αPM cells express more INS than cells before transplantation, but still maintained ARX expression. Although endogenous expression of human β-cell transcription factors (PDX1, MAFA and NKX6-1) was not changed significantly 7 days after transduction in vitro, their expression in αPM grafts was significantly increased after transplantation. Gene expression levels were normalized to GFP expression. *P < 0.05, **P < 0.01, ***P < 0.001 one-way ANOVA with Holm–Sidak’s multiple comparisons test. n = 3 different T2D donors for αGFP and αPM in vitro. n = 2 different T2D donors for graft of αPM + HM. p, Transmission electron micrographs of a β-cell in an engrafted T2D islet (left) and of two reprogrammed α-cells in engrafted αPM + HM pseudoislets (right). T2D β-cells do not contain abundant insulin granules, as previously reported61. Reprogrammed α-cells contain abundant β-like granules, with the typical crystalized dense core surrounded by a clear halo. q, TUNEL staining (green) showed very rare apoptosis events (less than 1%) in the graft of αPM + HM pseudoislets 9 weeks after transplantation. In g and h, non-grafted STZ mice (n = 4); STZ mice (n = 1) with αPM+HM graft from 2 donors; STZ mice (n = 1) with native islet graft; healthy mice (n = 2). Data are mean ± s.e.m. Scale bars, 25 μm (d, k, n, q) or 500 nm (p). Images are representative of three different T2D donors’ grafts (k, n, q), and two different T2D donors (p).
Extended Data Fig. 8 Transcriptomic analyses.
a, b, GSEA using the transcriptomes of sorted α-cells and αGFP pseudoislets. There is a significant enriched expression of genes associated with β-cell function such as mitochondrial ‘oxidative phosphorylation’ and ‘respiratory chain’ in αGFP pseudoislets compared to sorted α-cells (a). Heatmaps of transcriptomic expression levels of listed genes tested in GSEA (b). c, Top 15 canonical pathways that differ between sorted single α-cells and αGFP pseudoislets. Inflammatory/stress-related pathways were downregulated in αGFP pseudoislets compared to sorted α-cell singlets. Pathway analyses were done by IPA. d, Heatmaps of transcriptomic expression levels of gene sets in αPM pseudoislets and sorted α-cells (related to GSEA in Fig. 3g). e, Volcano plot representing DEGs in sorted α-cells compared to β-cells (FDR < 0.05, fold change > 2). In total, 887 α-cell-enriched genes were identified. The blue box delimitates α-cell-enriched genes (the ‘α-cell signature’ (Supplementary Table 14). f–h, Changes in expression of α-cell signature genes caused by reaggregation and PDX1 and MAFA effects. Volcano plots showing DEGs in αGFP pseudoislets relative to sorted α-cells (f) that characterize the cell reaggregation effect, in αPM pseudoislets relative to αGFP pseudoislets (g), reflecting the effect of PDX1 and MAFA expression, and in αPM pseudoislets relative to sorted α-cells (h), which reflects the combined effect of cell reaggregation and transcription factor expression. Downregulated DEGs in each condition (coloured squares on volcano plots) were overlapped with the α-cell-enriched gene list of e, as a measure of repressed α-cell signature. Venn diagrams show that 218 α-enriched genes are downregulated in α-cells upon aggregation (f, Supplementary Table 15), 120 genes upon PDX1 and MAFA activation (g, Supplementary Table 16), and in total 272 ‘α-like genes’ are downregulated in α-cells as a result of the combined effect of cell aggregation and transcription factor expression (h, Supplementary Table 17). DEGs: FDR < 0.05. i, Hierarchical clustering analysis of proteomes from in vitro samples reveal that αPM and β-cell pseudoislets cluster together. j, In vivo effect on αPM cells at 1 month after transplantation. DEGs (FDR < 0.05) between αPM pseudoislets before transplantation (n = 7) and in grafted αPM pseudoislets (n = 5) were analysed by IPA to identify downstream effects. Several pathways were activated, including ‘synthesis of hormone’, ‘secretion of secretory granules’ and ‘innervation’. See details in Supplementary Table 20. n = 5 grafts from 9 non-diabetic donors were retrieved from the mouse renal capsules, FACS sorted with GFP, and analysed by bulk RNA-seq. k, Effect of cell aggregation and reprogramming factor expression on human α-cell identity. Reaggregation of dispersed α-cells and expression of the transcription factors PDX1 and MAFA promotes the upregulation of a subset of β-cell-enriched genes (‘β-cell signature’), which is sufficient to confer GSIS to α-cells in monotypic αPM pseudoislets. Concomitantly, some but not all the α-cell-enriched genes (α-cell signature) are downregulated in αPM pseudoislets, leading to a hybrid α/β signature.
Extended Data Fig. 9 scRNA-seq.
a, Schematic of scRNA-seq analyses and pseudotemporal ordering. Monotypic pseudoislets containing labelled human α- (αGFP or αPM) or β-cells (βGFP) were cultured for 1 week and sorted into single cells. Microfluidic device encapsulated each cell individually with a barcoded primer bead in a droplet. cDNA libraries were constructed and sequenced. In silico cell-lineage reconstruction during reprogramming was performed by pseudotime analysis, to dissect the reprogramming path/trajectory. αGFP and βGFP pseudoislets were analysed as controls. b, GCG and INS expression on t-SNE of single-cell transcriptomes from αGFP (n = 47), αPM (n = 434) and βGFP cells (n = 51) from pseudoislets cultured for 1 week (related to Fig. 4a). c, Cell clustering of αPM cells (n = 434) based on the state along pseudotime trajectory (related to Fig. 4b), showing ten different states. Although four small branches were detected near the main path, most cells were distributed along main stem. d, INS and GCG expression on pseudotime trajectory of αPM cells (related to Fig. 4b). e, Cell distributions of pseudotime-based early, mid and late αPM cells on the t-SNE map (related to Fig. 4f). f, Kinetics of gene expression along pseudotime progression in αPM cells (n = 434) (related to Fig. 4e). g, ARX expression in cell clusters from t-SNE and pseudotemporal ordering analysis.
Extended Data Fig. 10 Evaluation of the specificity and cytotoxic properties of CTL clones.
a, Design of CTL killing assays. As target cells, monotypic pseudoislets (αGFP, αPM or βGFP) cultured for 1–2 weeks were dissociated and labelled with chromium (51Cr). In some control conditions, islet cells were loaded with either DRiP or PPI peptide epitopes. Then, target cells were co-cultured with effector cells (CTLs), which were either CMV-directed (negative control clones), DRiP-directed (targeting stressed β-cells), PPI-directed (recognizing preproinsulin), or alloreactive (HLA-A2; positive control) CTL clones at three different effector/target ratios. DRiP and PPI CTLs are autoreactive T cell clones derived from T1D patients. After co-culture for 4 h, the release of 51Cr from islet cells was measured with γ-counter to calculate the specific cell lysis. b, Schema of validation for CTLs. To evaluate the specificity and function of CTL clones, JY cells—an Epstein–Barr virus (EBV)-immortalized B lymphoblastoid cell line (HLA-A2+)—were used as target cells. As positive control, JY cells were loaded with either DRiP or PPI peptide epitope and labelled with 51Cr. Then they were co-cultured with effector cells (CTLs), which are CMV-directed, DRiP-directed, PPI-directed, or alloreactive (HLA-A2) CTL clones. c, CTL killing assay against JY cells. JY cells were killed by the alloreactive HLA-A2 CTLs, but not by CMV-directed CTLs, β-cell-specific CTLs anti-PPI or anti-DRiP CTLs. When target cells were loaded with the PPI or DRiP peptide epitope, JY cells were killed by the respective CTLs, confirming that the specific CTLs function and kill when they recognize their epitope. Each dot represents independent measurement from three independent experiments. ****P < 0.0001, one-way ANOVA with Holm–Sidak’s multiple comparisons test.
Supplementary information
Supplementary Information
This file contains a guide for Supplementary Tables 1-23 and Supplementary Data Figures 1-3.
Supplementary Figure 1
Representative immunostaining images with higher magnification of sorted α- or β-cells for INS/GCG/ SST/DAPI or INS/GCG/PPY /DAPI from 41 donors (related to Extended Data Fig. 1a). Immunostaining of sorted islet cells.
Supplementary Figure 2
Representative immunostaining of sorted α-cells for INS/GCG/SST from 41 donors (related to Extended Data Fig. 1a). The images were tile-scanned and merged. Scale bar: 250 μm. This file contains Tile scan picture of immunostaining in sorted α-cells.
Supplementary Figure 3
Representative immunostaining of sorted β-cells for INS/GCG/SST from 42 donors (related to Extended Data Fig. 1a). The images were tile-scanned and merged. Scale bar: 250 μm. Tile scan picture of immunostaining in sorted β-cells.
Supplementary Tables
This file contains Supplementary Tables 1-23.
Source data
Rights and permissions
About this article
Cite this article
Furuyama, K., Chera, S., van Gurp, L. et al. Diabetes relief in mice by glucose-sensing insulin-secreting human α-cells. Nature 567, 43–48 (2019). https://doi.org/10.1038/s41586-019-0942-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41586-019-0942-8
This article is cited by
-
Novel time-resolved reporter mouse reveals spatial and transcriptional heterogeneity during alpha cell differentiation
Diabetologia (2024)
-
Stomach-derived human insulin-secreting organoids restore glucose homeostasis
Nature Cell Biology (2023)
-
Comparative Study of Two Common In Vitro Models for the Pancreatic Islet with MIN6
Tissue Engineering and Regenerative Medicine (2023)
-
Direct reprogramming of human fibroblasts into insulin-producing cells using transcription factors
Communications Biology (2023)
-
Isolation of mouse pancreatic islet Procr+ progenitors and long-term expansion of islet organoids in vitro
Nature Protocols (2022)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
